---------------------------------------------------------------------------------------------------------------------------------------------------------
      name:  <unnamed>
       log:  C:\Users\Eric Zou\Dropbox\replicate_smokelabor/2_analysis/output_logs/figure3.log
  log type:  text
 opened on:   3 Aug 2022, 09:57:19

. 
.         use "$Rep_smokelabor/1_build/regdata/county_quarter.dta", clear 

.         tsset fe_countyqtroy rfrnc_yr
       panel variable:  fe_countyqtroy (strongly balanced)
        time variable:  rfrnc_yr, 2006 to 2019
                delta:  1 unit

.         
.         ** panel a 
.         if 1 {
.                 * static 
.                 reghdfe pm25 hms_deep [aw=seer_pop], a(fe_countyqtroy fe_styr) vce(cluster countyfip fe_stqtros)
(dropped 189 singleton observations)
(MWFE estimator converged in 8 iterations)

HDFE Linear regression                            Number of obs   =     75,207
Absorbing 2 HDFE groups                           F(   1,   1685) =      71.70
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.7864
                                                  Adj R-squared   =     0.7634
Number of clusters (countyfip) =      1,686       Within R-sq.    =     0.0369
Number of clusters (fe_stqtros) =      2,548      Root MSE        =     1.3481

               (Std. Err. adjusted for 1,686 clusters in countyfip fe_stqtros)
------------------------------------------------------------------------------
             |               Robust
        pm25 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    hms_deep |   .0556401   .0065709     8.47   0.000      .042752    .0685282
       _cons |   9.190593    .040427   227.34   0.000     9.111301    9.269886
------------------------------------------------------------------------------

Absorbed degrees of freedom:
--------------------------------------------------------+
    Absorbed FE | Categories  - Redundant  = Num. Coefs |
----------------+---------------------------------------|
 fe_countyqtroy |      6677        6677           0    *|
        fe_styr |       637           0         637     |
--------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
.                 local s_b=_b[hms_deep]
.                 local s_l=_b[hms_deep] - invttail(e(df_r),0.025)*_se[hms_deep]
.                 local s_u=_b[hms_deep] + invttail(e(df_r),0.025)*_se[hms_deep]
.                 
.                 * dynamic 
.                 reghdfe pm25 F2.hms_deep F1.hms_deep hms_deep L1.hms_deep L2.hms_deep [aw=seer_pop], a(fe_countyqtroy fe_styr) vce(cluster countyfip fe
> _stqtros)
(dropped 198 singleton observations)
(MWFE estimator converged in 9 iterations)

HDFE Linear regression                            Number of obs   =     51,532
Absorbing 2 HDFE groups                           F(   5,   1624) =      18.75
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.7826
                                                  Adj R-squared   =     0.7491
Number of clusters (countyfip) =      1,625       Within R-sq.    =     0.0409
Number of clusters (fe_stqtros) =      1,764      Root MSE        =     1.2641

               (Std. Err. adjusted for 1,625 clusters in countyfip fe_stqtros)
------------------------------------------------------------------------------
             |               Robust
        pm25 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    hms_deep |
         F2. |   .0020702   .0048444     0.43   0.669    -.0074317     .011572
         F1. |   .0015988   .0052781     0.30   0.762    -.0087537    .0119514
         --. |   .0531002   .0058353     9.10   0.000     .0416547    .0645457
         L1. |  -.0059751   .0060804    -0.98   0.326    -.0179014    .0059512
         L2. |   .0091425   .0061729     1.48   0.139    -.0029651    .0212501
             |
       _cons |   8.967583   .0845539   106.06   0.000     8.801737     9.13343
------------------------------------------------------------------------------

Absorbed degrees of freedom:
--------------------------------------------------------+
    Absorbed FE | Categories  - Redundant  = Num. Coefs |
----------------+---------------------------------------|
 fe_countyqtroy |      6451        6451           0    *|
        fe_styr |       441           0         441     |
--------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
.                 local d_b_m2=_b[F2.hms_deep]
.                 local d_l_m2=_b[F2.hms_deep] - invttail(e(df_r),0.025)*_se[F2.hms_deep]
.                 local d_u_m2=_b[F2.hms_deep] + invttail(e(df_r),0.025)*_se[F2.hms_deep]
.                 
.                 local d_b_m1=_b[F.hms_deep]
.                 local d_l_m1=_b[F.hms_deep] - invttail(e(df_r),0.025)*_se[F.hms_deep]
.                 local d_u_m1=_b[F.hms_deep] + invttail(e(df_r),0.025)*_se[F.hms_deep]
.                 
.                 local d_b_p0=_b[hms_deep]
.                 local d_l_p0=_b[hms_deep] - invttail(e(df_r),0.025)*_se[hms_deep]
.                 local d_u_p0=_b[hms_deep] + invttail(e(df_r),0.025)*_se[hms_deep]
.                 
.                 local d_b_p1=_b[L.hms_deep]
.                 local d_l_p1=_b[L.hms_deep] - invttail(e(df_r),0.025)*_se[L.hms_deep]
.                 local d_u_p1=_b[L.hms_deep] + invttail(e(df_r),0.025)*_se[L.hms_deep]
.                 
.                 local d_b_p2=_b[L2.hms_deep]
.                 local d_l_p2=_b[L2.hms_deep] - invttail(e(df_r),0.025)*_se[L2.hms_deep]
.                 local d_u_p2=_b[L2.hms_deep] + invttail(e(df_r),0.025)*_se[L2.hms_deep]
.                 
.                 * plot 
.                 preserve 
.                         clear 
.                         set obs 5
number of observations (_N) was 0, now 5
.                         
.                         gen eqtr=_n-3
.                         foreach var in d_b d_l d_u {
  2.                                 gen `var'=. 
  3.                                 replace `var' = ``var'_m2' if eqtr==-2 
  4.                                 replace `var' = ``var'_m1' if eqtr==-1 
  5.                                 replace `var' = ``var'_p0' if eqtr== 0 
  6.                                 replace `var' = ``var'_p1' if eqtr== 1 
  7.                                 replace `var' = ``var'_p2' if eqtr== 2 
  8.                         }
(5 missing values generated)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(5 missing values generated)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(5 missing values generated)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
.                         
.                         foreach var in s_b s_l s_u {
  2.                                 gen `var'=. 
  3.                                 replace `var'=``var'' if eqtr==0
  4.                         }
(5 missing values generated)
(1 real change made)
(5 missing values generated)
(1 real change made)
(5 missing values generated)
(1 real change made)
.                         
.                         tw      connected d_b eqtr, col(teal) lw(0.5) msymbol(circle_hollow) || ///
>                                 line d_l d_u eqtr, col(gs9 gs9) lp(dash dash) || ///
>                                 rcap s_l s_u eqtr, col(blue) || ///
>                                 scatter s_b eqtr, col(blue) ///
>                                 xtitle("Event year",size(huge)) ytitle("PM2.5",size(huge)) ///
>                                 ylab(,labsize(vlarge) nogrid) yline(0,lp(dot) lw(0.6) lcol(black)) ///
>                                 xlab(,labsize(vlarge)) ///
>                                 legend(ring(0) pos(2) col(1) size(large) order(1 "dynamic" 5 "static")) ///
>                                 graphregion(color(white)) xsize(4.5)
.                         gr export "$Rep_smokelabor/2_analysis/output_figures/figure3_a.pdf", replace 
(file C:\Users\Eric Zou\Dropbox\replicate_smokelabor/2_analysis/output_figures/figure3_a.pdf written in PDF format)
.                 restore 
.         
.         }

.         
.         ** panel b 
.         if 1 {
.                 
.                 * static 
.                 reghdfe d_pc_qwi_payroll hms_deep [aw=seer_pop], a(fe_countyqtroy fe_styr) vce(cluster countyfip fe_stqtros)
(MWFE estimator converged in 5 iterations)

HDFE Linear regression                            Number of obs   =    160,346
Absorbing 2 HDFE groups                           F(   1,   2519) =      45.21
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.2431
                                                  Adj R-squared   =     0.1760
Number of clusters (countyfip) =      3,106       Within R-sq.    =     0.0038
Number of clusters (fe_stqtros) =      2,520      Root MSE        =   409.6540

               (Std. Err. adjusted for 2,520 clusters in countyfip fe_stqtros)
------------------------------------------------------------------------------
             |               Robust
d_pc_qwi_p~l |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    hms_deep |  -5.216626   .7758234    -6.72   0.000    -6.737943   -3.695309
       _cons |    166.374   4.714186    35.29   0.000       157.13    175.6181
------------------------------------------------------------------------------

Absorbed degrees of freedom:
--------------------------------------------------------+
    Absorbed FE | Categories  - Redundant  = Num. Coefs |
----------------+---------------------------------------|
 fe_countyqtroy |     12424       12424           0    *|
        fe_styr |       631           0         631     |
--------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
.                 local s_b=_b[hms_deep]
.                 local s_l=_b[hms_deep] - invttail(e(df_r),0.025)*_se[hms_deep]
.                 local s_u=_b[hms_deep] + invttail(e(df_r),0.025)*_se[hms_deep]
.                 
.                 * dynamic 
.                 reghdfe d_pc_qwi_payroll F2.hms_deep F1.hms_deep hms_deep L1.hms_deep L2.hms_deep [aw=seer_pop], a(fe_countyqtroy fe_styr) vce(cluster 
> countyfip fe_stqtros)
(MWFE estimator converged in 4 iterations)

HDFE Linear regression                            Number of obs   =    111,704
Absorbing 2 HDFE groups                           F(   5,   1755) =      18.86
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.2105
                                                  Adj R-squared   =     0.1077
Number of clusters (countyfip) =      3,106       Within R-sq.    =     0.0076
Number of clusters (fe_stqtros) =      1,756      Root MSE        =   454.3441

               (Std. Err. adjusted for 1,756 clusters in countyfip fe_stqtros)
------------------------------------------------------------------------------
             |               Robust
d_pc_qwi_p~l |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    hms_deep |
         F2. |   1.273485   .7521619     1.69   0.091    -.2017431    2.748712
         F1. |   1.725584   .7461871     2.31   0.021     .2620749    3.189093
         --. |  -7.496441   1.215841    -6.17   0.000    -9.881091   -5.111792
         L1. |   2.892616   .8811439     3.28   0.001     1.164414    4.620818
         L2. |  -1.446644   .8039777    -1.80   0.072    -3.023498    .1302112
             |
       _cons |   128.3972   11.45579    11.21   0.000     105.9288    150.8656
------------------------------------------------------------------------------

Absorbed degrees of freedom:
--------------------------------------------------------+
    Absorbed FE | Categories  - Redundant  = Num. Coefs |
----------------+---------------------------------------|
 fe_countyqtroy |     12424       12424           0    *|
        fe_styr |       439           0         439     |
--------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
.                 local d_b_m2=_b[F2.hms_deep]
.                 local d_l_m2=_b[F2.hms_deep] - invttail(e(df_r),0.025)*_se[F2.hms_deep]
.                 local d_u_m2=_b[F2.hms_deep] + invttail(e(df_r),0.025)*_se[F2.hms_deep]
.                 
.                 local d_b_m1=_b[F.hms_deep]
.                 local d_l_m1=_b[F.hms_deep] - invttail(e(df_r),0.025)*_se[F.hms_deep]
.                 local d_u_m1=_b[F.hms_deep] + invttail(e(df_r),0.025)*_se[F.hms_deep]
.                 
.                 local d_b_p0=_b[hms_deep]
.                 local d_l_p0=_b[hms_deep] - invttail(e(df_r),0.025)*_se[hms_deep]
.                 local d_u_p0=_b[hms_deep] + invttail(e(df_r),0.025)*_se[hms_deep]
.                 
.                 local d_b_p1=_b[L.hms_deep]
.                 local d_l_p1=_b[L.hms_deep] - invttail(e(df_r),0.025)*_se[L.hms_deep]
.                 local d_u_p1=_b[L.hms_deep] + invttail(e(df_r),0.025)*_se[L.hms_deep]
.                 
.                 local d_b_p2=_b[L2.hms_deep]
.                 local d_l_p2=_b[L2.hms_deep] - invttail(e(df_r),0.025)*_se[L2.hms_deep]
.                 local d_u_p2=_b[L2.hms_deep] + invttail(e(df_r),0.025)*_se[L2.hms_deep]
.                 
.                 * plot 
.                 preserve 
.                         clear 
.                         set obs 5
number of observations (_N) was 0, now 5
.                         
.                         gen eqtr=_n-3
.                         foreach var in d_b d_l d_u {
  2.                                 gen `var'=. 
  3.                                 replace `var' = ``var'_m2' if eqtr==-2 
  4.                                 replace `var' = ``var'_m1' if eqtr==-1 
  5.                                 replace `var' = ``var'_p0' if eqtr== 0 
  6.                                 replace `var' = ``var'_p1' if eqtr== 1 
  7.                                 replace `var' = ``var'_p2' if eqtr== 2 
  8.                         }
(5 missing values generated)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(5 missing values generated)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(5 missing values generated)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
.                         
.                         foreach var in s_b s_l s_u {
  2.                                 gen `var'=. 
  3.                                 replace `var'=``var'' if eqtr==0
  4.                         }
(5 missing values generated)
(1 real change made)
(5 missing values generated)
(1 real change made)
(5 missing values generated)
(1 real change made)
.                         
.                         tw      connected d_b eqtr, col(teal) lw(0.5) msymbol(circle_hollow) || ///
>                                 line d_l d_u eqtr, col(gs9 gs9) lp(dash dash) || ///
>                                 rcap s_l s_u eqtr, col(blue) || ///
>                                 scatter s_b eqtr, col(blue) ///
>                                 xtitle("Event year",size(huge)) ytitle("Income per capita",size(huge)) ///
>                                 ylab(,labsize(vlarge) nogrid) yline(0,lp(dot) lw(0.6) lcol(black)) ///
>                                 xlab(,labsize(vlarge)) ///
>                                 legend(ring(0) pos(5) col(1) size(large) order(1 "dynamic" 5 "static")) ///
>                                 graphregion(color(white)) xsize(4.5)
.                         gr export "$Rep_smokelabor/2_analysis/output_figures/figure3_b.pdf", replace 
(file C:\Users\Eric Zou\Dropbox\replicate_smokelabor/2_analysis/output_figures/figure3_b.pdf written in PDF format)
.                 restore 
.         
.         }

.                         
.         ** panel c 
.         if 1 {
.                 
.                 * static 
.                 reghdfe d_pmil_qwi_emptotal hms_deep [aw=seer_pop16plus], a(fe_countyqtroy fe_styr) vce(cluster countyfip fe_stqtros)
(MWFE estimator converged in 5 iterations)

HDFE Linear regression                            Number of obs   =    160,346
Absorbing 2 HDFE groups                           F(   1,   2519) =      13.20
Statistics robust to heteroskedasticity           Prob > F        =     0.0003
                                                  R-squared       =     0.5293
                                                  Adj R-squared   =     0.4875
Number of clusters (countyfip) =      3,106       Within R-sq.    =     0.0005
Number of clusters (fe_stqtros) =      2,520      Root MSE        = 18020.2635

               (Std. Err. adjusted for 2,520 clusters in countyfip fe_stqtros)
------------------------------------------------------------------------------
             |               Robust
d_pmil_qwi~l |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    hms_deep |  -79.58788   21.90873    -3.63   0.000    -122.5488   -36.62692
       _cons |  -1395.351   134.9056   -10.34   0.000    -1659.888   -1130.813
------------------------------------------------------------------------------

Absorbed degrees of freedom:
--------------------------------------------------------+
    Absorbed FE | Categories  - Redundant  = Num. Coefs |
----------------+---------------------------------------|
 fe_countyqtroy |     12424       12424           0    *|
        fe_styr |       631           0         631     |
--------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
.                 local s_b=_b[hms_deep]
.                 local s_l=_b[hms_deep] - invttail(e(df_r),0.025)*_se[hms_deep]
.                 local s_u=_b[hms_deep] + invttail(e(df_r),0.025)*_se[hms_deep]
.                 
.                 * dynamic 
.                 reghdfe d_pmil_qwi_emptotal F2.hms_deep F1.hms_deep hms_deep L1.hms_deep L2.hms_deep [aw=seer_pop16plus], a(fe_countyqtroy fe_styr) vce
> (cluster countyfip fe_stqtros)
(MWFE estimator converged in 4 iterations)

HDFE Linear regression                            Number of obs   =    111,704
Absorbing 2 HDFE groups                           F(   5,   1755) =       8.09
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.5795
                                                  Adj R-squared   =     0.5247
Number of clusters (countyfip) =      3,106       Within R-sq.    =     0.0019
Number of clusters (fe_stqtros) =      1,756      Root MSE        = 18235.6750

               (Std. Err. adjusted for 1,756 clusters in countyfip fe_stqtros)
------------------------------------------------------------------------------
             |               Robust
d_pmil_qwi~l |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    hms_deep |
         F2. |   27.46214   24.63849     1.11   0.265    -20.86174    75.78602
         F1. |   67.90371   27.99174     2.43   0.015     13.00306    122.8044
         --. |  -131.6437   28.87918    -4.56   0.000    -188.2849   -75.00248
         L1. |    93.6281   21.49745     4.36   0.000      51.4648    135.7914
         L2. |  -56.97882   26.17001    -2.18   0.030    -108.3065   -5.651139
             |
       _cons |  -1353.381    325.627    -4.16   0.000    -1992.039   -714.7236
------------------------------------------------------------------------------

Absorbed degrees of freedom:
--------------------------------------------------------+
    Absorbed FE | Categories  - Redundant  = Num. Coefs |
----------------+---------------------------------------|
 fe_countyqtroy |     12424       12424           0    *|
        fe_styr |       439           0         439     |
--------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
.                 local d_b_m2=_b[F2.hms_deep]
.                 local d_l_m2=_b[F2.hms_deep] - invttail(e(df_r),0.025)*_se[F2.hms_deep]
.                 local d_u_m2=_b[F2.hms_deep] + invttail(e(df_r),0.025)*_se[F2.hms_deep]
.                 
.                 local d_b_m1=_b[F.hms_deep]
.                 local d_l_m1=_b[F.hms_deep] - invttail(e(df_r),0.025)*_se[F.hms_deep]
.                 local d_u_m1=_b[F.hms_deep] + invttail(e(df_r),0.025)*_se[F.hms_deep]
.                 
.                 local d_b_p0=_b[hms_deep]
.                 local d_l_p0=_b[hms_deep] - invttail(e(df_r),0.025)*_se[hms_deep]
.                 local d_u_p0=_b[hms_deep] + invttail(e(df_r),0.025)*_se[hms_deep]
.                 
.                 local d_b_p1=_b[L.hms_deep]
.                 local d_l_p1=_b[L.hms_deep] - invttail(e(df_r),0.025)*_se[L.hms_deep]
.                 local d_u_p1=_b[L.hms_deep] + invttail(e(df_r),0.025)*_se[L.hms_deep]
.                 
.                 local d_b_p2=_b[L2.hms_deep]
.                 local d_l_p2=_b[L2.hms_deep] - invttail(e(df_r),0.025)*_se[L2.hms_deep]
.                 local d_u_p2=_b[L2.hms_deep] + invttail(e(df_r),0.025)*_se[L2.hms_deep]
.                 
.                 * plot 
.                 preserve 
.                         clear 
.                         set obs 5
number of observations (_N) was 0, now 5
.                         
.                         gen eqtr=_n-3
.                         foreach var in d_b d_l d_u {
  2.                                 gen `var'=. 
  3.                                 replace `var' = ``var'_m2' if eqtr==-2 
  4.                                 replace `var' = ``var'_m1' if eqtr==-1 
  5.                                 replace `var' = ``var'_p0' if eqtr== 0 
  6.                                 replace `var' = ``var'_p1' if eqtr== 1 
  7.                                 replace `var' = ``var'_p2' if eqtr== 2 
  8.                         }
(5 missing values generated)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(5 missing values generated)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(5 missing values generated)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
.                         
.                         foreach var in s_b s_l s_u {
  2.                                 gen `var'=. 
  3.                                 replace `var'=``var'' if eqtr==0
  4.                         }
(5 missing values generated)
(1 real change made)
(5 missing values generated)
(1 real change made)
(5 missing values generated)
(1 real change made)
.                         
.                         
.                         tw      connected d_b eqtr, col(teal) lw(0.5) msymbol(circle_hollow) || ///
>                                 line d_l d_u eqtr, col(gs9 gs9) lp(dash dash) || ///
>                                 rcap s_l s_u eqtr, col(blue) || ///
>                                 scatter s_b eqtr, col(blue) ///
>                                 xtitle("Event year",size(huge)) ytitle("Employed per million",size(huge)) ///
>                                 ylab(,labsize(vlarge) nogrid) yline(0,lp(dot) lw(0.6) lcol(black)) ///
>                                 xlab(,labsize(vlarge)) ///
>                                 legend(ring(0) pos(5) col(1) size(large) order(1 "dynamic" 5 "static")) ///
>                                 graphregion(color(white)) xsize(4.5)
.                         gr export "$Rep_smokelabor/2_analysis/output_figures/figure3_c.pdf", replace 
(file C:\Users\Eric Zou\Dropbox\replicate_smokelabor/2_analysis/output_figures/figure3_c.pdf written in PDF format)
.                 restore 
.                 
.                 
.         }

.         
.         ** panel d 
.         if 1 {
.                 
.                 * static 
.                 reghdfe d_pmil_lau_lfp hms_deep [aw=seer_pop], a(fe_countyqtroy fe_styr) vce(cluster countyfip fe_stqtros)
(MWFE estimator converged in 4 iterations)

HDFE Linear regression                            Number of obs   =    161,498
Absorbing 2 HDFE groups                           F(   1,   2547) =      17.57
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.2403
                                                  Adj R-squared   =     0.1735
Number of clusters (countyfip) =      3,106       Within R-sq.    =     0.0003
Number of clusters (fe_stqtros) =      2,548      Root MSE        = 11151.6317

               (Std. Err. adjusted for 2,548 clusters in countyfip fe_stqtros)
------------------------------------------------------------------------------
             |               Robust
d_pmil_lau~p |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    hms_deep |  -38.70033   9.231539    -4.19   0.000    -56.80242   -20.59825
       _cons |  -1941.697   64.50074   -30.10   0.000    -2068.176   -1815.217
------------------------------------------------------------------------------

Absorbed degrees of freedom:
--------------------------------------------------------+
    Absorbed FE | Categories  - Redundant  = Num. Coefs |
----------------+---------------------------------------|
 fe_countyqtroy |     12424       12424           0    *|
        fe_styr |       637           0         637     |
--------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
.                 local s_b=_b[hms_deep]
.                 local s_l=_b[hms_deep] - invttail(e(df_r),0.025)*_se[hms_deep]
.                 local s_u=_b[hms_deep] + invttail(e(df_r),0.025)*_se[hms_deep]
.                 
.                 * dynamic 
.                 reghdfe d_pmil_lau_lfp F2.hms_deep F1.hms_deep hms_deep L1.hms_deep L2.hms_deep [aw=seer_pop], a(fe_countyqtroy fe_styr) vce(cluster co
> untyfip fe_stqtros)
(MWFE estimator converged in 4 iterations)
Warning: VCV matrix was non-positive semi-definite; adjustment from Cameron, Gelbach & Miller applied.

HDFE Linear regression                            Number of obs   =    111,816
Absorbing 2 HDFE groups                           F(   5,   1763) =     184.79
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.2489
                                                  Adj R-squared   =     0.1512
Number of clusters (countyfip) =      3,106       Within R-sq.    =     0.0002
Number of clusters (fe_stqtros) =      1,764      Root MSE        = 12167.6211

               (Std. Err. adjusted for 1,764 clusters in countyfip fe_stqtros)
------------------------------------------------------------------------------
             |               Robust
d_pmil_lau~p |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    hms_deep |
         F2. |   13.36355   11.52038     1.16   0.246    -9.231502     35.9586
         F1. |    21.7784   3.102547     7.02   0.000     15.69334    27.86346
         --. |  -17.33832   10.33058    -1.68   0.093     -37.5998    2.923151
         L1. |   9.025381   13.76485     0.66   0.512    -17.97176    36.02252
         L2. |    22.6293   14.50293     1.56   0.119    -5.815445    51.07404
             |
       _cons |  -3412.479   137.3163   -24.85   0.000    -3681.799    -3143.16
------------------------------------------------------------------------------

Absorbed degrees of freedom:
--------------------------------------------------------+
    Absorbed FE | Categories  - Redundant  = Num. Coefs |
----------------+---------------------------------------|
 fe_countyqtroy |     12424       12424           0    *|
        fe_styr |       441           0         441     |
--------------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation
.                 local d_b_m2=_b[F2.hms_deep]
.                 local d_l_m2=_b[F2.hms_deep] - invttail(e(df_r),0.025)*_se[F2.hms_deep]
.                 local d_u_m2=_b[F2.hms_deep] + invttail(e(df_r),0.025)*_se[F2.hms_deep]
.                 
.                 local d_b_m1=_b[F.hms_deep]
.                 local d_l_m1=_b[F.hms_deep] - invttail(e(df_r),0.025)*_se[F.hms_deep]
.                 local d_u_m1=_b[F.hms_deep] + invttail(e(df_r),0.025)*_se[F.hms_deep]
.                 
.                 local d_b_p0=_b[hms_deep]
.                 local d_l_p0=_b[hms_deep] - invttail(e(df_r),0.025)*_se[hms_deep]
.                 local d_u_p0=_b[hms_deep] + invttail(e(df_r),0.025)*_se[hms_deep]
.                 
.                 local d_b_p1=_b[L.hms_deep]
.                 local d_l_p1=_b[L.hms_deep] - invttail(e(df_r),0.025)*_se[L.hms_deep]
.                 local d_u_p1=_b[L.hms_deep] + invttail(e(df_r),0.025)*_se[L.hms_deep]
.                 
.                 local d_b_p2=_b[L2.hms_deep]
.                 local d_l_p2=_b[L2.hms_deep] - invttail(e(df_r),0.025)*_se[L2.hms_deep]
.                 local d_u_p2=_b[L2.hms_deep] + invttail(e(df_r),0.025)*_se[L2.hms_deep]
.                 
.                 * plot 
.                 preserve 
.                         clear 
.                         set obs 5
number of observations (_N) was 0, now 5
.                         
.                         gen eqtr=_n-3
.                         foreach var in d_b d_l d_u {
  2.                                 gen `var'=. 
  3.                                 replace `var' = ``var'_m2' if eqtr==-2 
  4.                                 replace `var' = ``var'_m1' if eqtr==-1 
  5.                                 replace `var' = ``var'_p0' if eqtr== 0 
  6.                                 replace `var' = ``var'_p1' if eqtr== 1 
  7.                                 replace `var' = ``var'_p2' if eqtr== 2 
  8.                         }
(5 missing values generated)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(5 missing values generated)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(5 missing values generated)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
(1 real change made)
.                         
.                         foreach var in s_b s_l s_u {
  2.                                 gen `var'=. 
  3.                                 replace `var'=``var'' if eqtr==0
  4.                         }
(5 missing values generated)
(1 real change made)
(5 missing values generated)
(1 real change made)
(5 missing values generated)
(1 real change made)
.                         
.                         
.                         tw      connected d_b eqtr, col(teal) lw(0.5) msymbol(circle_hollow) || ///
>                                 line d_l d_u eqtr, col(gs9 gs9) lp(dash dash) || ///
>                                 rcap s_l s_u eqtr, col(blue) || ///
>                                 scatter s_b eqtr, col(blue) ///
>                                 xtitle("Event year",size(huge)) ytitle("LFP per million",size(huge)) ///
>                                 ylab(,labsize(vlarge) nogrid) yline(0,lp(dot) lw(0.6) lcol(black)) ///
>                                 xlab(,labsize(vlarge)) ///
>                                 legend(ring(0) pos(5) col(1) size(large) order(1 "dynamic" 5 "static")) ///
>                                 graphregion(color(white)) xsize(4.5)
.                         gr export "$Rep_smokelabor/2_analysis/output_figures/figure3_d.pdf", replace 
(file C:\Users\Eric Zou\Dropbox\replicate_smokelabor/2_analysis/output_figures/figure3_d.pdf written in PDF format)
.                 restore 
.         
.         }

.                         
. log close
      name:  <unnamed>
       log:  C:\Users\Eric Zou\Dropbox\replicate_smokelabor/2_analysis/output_logs/figure3.log
  log type:  text
 closed on:   3 Aug 2022, 09:57:39
---------------------------------------------------------------------------------------------------------------------------------------------------------
